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Using Random Local Search Helps in Avoiding Local Optimum in Differential Evolution

Fulltext:


Research group:


Publication Type:

Conference/Workshop Paper

Venue:

IASTED International Conference on Artificial Intrelligence and Applications


Abstract

Differential Evolution is a stochastic and metaheuristic technique that has been proved powerful for solving real valued optimization problems in high dimensional spaces. However, Differential Evolution does not guarantee to con verge to the global optimum and it is easily to become trapped in a local optimum. In this paper, we aim to enhance Differential Evolution with Random Local Search to increase its ability to avoid local optimum. The proposed new algorithm is called Differential Evolution with Random Local Search (DERLS). The advantage of Random Local Search used in DERLS is that it is simple and fast in computation. The results of experiments have demonstrated that our DERLS algorithm can bring appreciable improvement for the acquired solutions in difficult optimization problems.

Bibtex

@inproceedings{Leon Ortiz3495,
author = {Miguel Leon Ortiz and Ning Xiong},
title = {Using Random Local Search Helps in Avoiding Local Optimum in Differential Evolution},
month = {February},
year = {2014},
booktitle = {IASTED International Conference on Artificial Intrelligence and Applications },
url = {http://www.es.mdh.se/publications/3495-}
}